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Heterogeneous Graph Convolutional Network
论文分享报告 Heterogeneous Graph Convolutional Network 乔子越
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Reference Kipf, Thomas N., and Max Welling. “Semi-supervised classification with graph convolutional networks.” arXiv preprint arXiv: (2016). (ICLR 2017) Schlichtkrull, M., Kipf, T. N., Bloem, P., Van Den Berg, R., Titov, I., & Welling, M. (2018, June). Modeling relational data with graph convolutional networks. In European Semantic Web Conference (pp ). Springer, Cham.
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Graph Convolutional Network[1]
Graph Convolutional Network and related graph neural network models can be understood as special cases of a simple differentiable message passing model that operate on local graph neighborhoods to concatenate self embedding and neighbor embedding to generate each node’s embedding.
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Graph Convolutional Network
Aggregator (Convolution layer): 1) Overall: 2) Node level: A tilde N(u) denotes the degree of node u
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Graph Convolutional Network
We can feed these embeddings into any loss function and run stochastic gradient descent to train the aggregation parameters. Output model: Task specific. 1) supervised task (e.g., node classification): where 2) unsupervised task (network embedding) Random walks (node2vec, DeepWalk) Graph factorization i.e., train the model so that “similar” nodes have similar embeddings.
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Relational Graph Convolutional Network[2]
Applied on graph with multiple relations (heterogeneous network) Aggregator (Convolution layer): Output model: 1) Link prediction (recovery of missing facts, i.e. subject-predicate-object triples) 2) entity classification (recovery of missing entity attributes) Two supervised task, no solution for heterogeneous network embedding(unsupervised task).
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Heterogeneous Graph Convolutional Network Embedding
Aggregator (Convolution layer): Output model: RW: paths generated by meta-path and relation weight guided random walk
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Heterogeneous Graph Convolutional Network Embedding
The application of HGCN embedding on author name disambiguation:
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